|
|
|
|
|
Issue #193
THE ML ENGINEER 🤖
|
|
|
|
|
|
| |
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
|
|
|
| |
|
|
| |
This week in the MLE #193:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
|
|
|
| |
|
|
|
|
| |
Responsible AI best practices are now growingly critical as Machine Learning becomes growingly ubiquitous in cross-industry use-cases at higher impact and scale. Because of this, we are thrilled to be contributing to this year's NeurIPS 2022 "Deploy & Monitor ML" workshop, where key insights will be shared on best-practices across security, privacy, data-centricity and beyond.
|
|
|
| |
|
|
| |
At Uber, millions of machine learning (ML) predictions are made every second, and hundreds of applied scientists, engineers, product managers, and researchers work on ML solutions daily. Uber mentions they "win by scaling machine learning", and in this interesting article they share how they have scaled education of machine learning across their workforce, including core principles, topics and plans.
|
|
|
| |
|
|
| |
Designing scalable systems continues to be highlighted as one of the subjects that machine learning practitioners struggle to build the strong capabilities required when the AI system use-cases start reaching certain scale. This free course provides a great introduction into the typical set of concepts covered in general systems design, together with practical examples with common system design exercises.
|
|
|
| |
|
|
| |
Open source strategies become growingly critical across every organisation, and consistently it's highlighted as a challenging value proposition to verbalise and quantify. Food delivery service Wolt has put together a great overview of their open source ecosystem around data, ML & observability, as well as the core principles that drive their involvement, adoption and contributions. They also cover a success story and a set of plans for long term involvement in OSS.
|
|
|
| |
|
|
| |
In order to have a comprehensive data protection and privacy policy, organizations must ensure the confidentiality and integrity of your data in these states: at rest, in use, and in transit. This post provides a comprehensive assessment of the state of confidential computing, including an overview of practical demand vs supply relative to the different topics in this field.
|
|
|
| |
|
|
|
|
|
|
| |
Upcoming MLOps Events
The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.
Conferences we'll be speaking at:
Other relevant upcoming MLOps conferences:
|
|
|
| |
|
|
| |
Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below.
- Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
- CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
- Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
If you know of any open source and open community events that are not listed do give us a heads up so we can add them!
|
|
|
| |
|
|
| |
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
- MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
- An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
- ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
|
|
|
| |
|
|
| |
|
© 2018 The Institute for Ethical AI & Machine Learning
|
| |
|
|
|
The Institute for Ethical AI & Machine Learning
